Weiwei Fang

VMPlanner: Optimizing virtual machine placement and traffic flow routing to reduce network power costs in cloud data centers

In recent years, the power costs of cloud data centers have become a practical concern and have attracted significant attention from both industry and academia. Most of the early works on data center energy efficiency have focused on the biggest power consumers (i.e., computer servers and cooling systems), yet without taking the networking part into consideration. However, recent studies have revealed that the network elements consume 10-20% of the total power in the data center, which poses a great challenge to effectively reducing network power cost without adversely affecting overall network performance. Based on the analysis on topology characteristics and traffic patterns of data centers, this paper presents a novel approach, called VMPlanner, for network power reduction in the virtualization-based data centers. The basic idea of VMPlanner is to optimize both virtual machine placement and traffic flow routing so as to turn off as many unneeded network elements as possible for power saving. We formulate the optimization problem, analyze its hardness, and solve it by designing VMPlanner as a stepwise optimization approach with three approximation algorithms. VMPlanner is implemented and evaluated in a simulated environment with traffic traces collected from a data center test-bed, and the experiment results illustrate the efficacy and efficiency of this approach.

Congestion avoidance, detection and alleviation in wireless sensor networks

Congestion in wireless sensor networks (WSNs) not only causes severe information loss but also leads to excessive energy consumption. To address this problem, a novel scheme for congestion avoidance, detection and alleviation (CADA) in WSNs is proposed in this paper. By exploiting data characteristics, a small number of representative nodes are chosen from those in the event area as data sources, so that the source traffic can be suppressed proactively to avoid potential congestion. Once congestion occurs inevitably due to traffic mergence, it will be detected in a timely way by the hotspot node based on a combination of buffer occupancy and channel utilization. Congestion is then alleviated reactively by either dynamic traffic multiplexing or source rate regulation in accordance with the specific hotspot scenarios. Extensive simulation results under typical congestion scenarios are presented to illuminate the distinguished performance of the proposed scheme.

Energy-efficient cooperative communication for data transmission in wireless sensor networks

It is a practical challenge to provide reliable and efficient communication for data transmission in wireless sensor networks. To recover from packet losses, conventional approaches tried to use retransmission or FEC mechanisms. However, these mechanisms may introduce excessive energy overhead for reliability guarantee. By exploiting the wireless broadcast nature and the node overhearing capability, we propose a novel cooperative communication scheme EECC to improve data transmission performance for wireless sensor networks. In this scheme, cooperative reply is performed at each hop by the best-suited node elected from those that have successfully overheard the transmitted packet. EECC is not a routing protocol but rather works as an augment to minimize the impact of packet losses on network performance. Extensive analytical and experimental results confirm that our scheme is very effective in improving both energy efficiency and end-to-end delay for data transmission in lossy networks.

On the throughput-energy tradeoff for data transmission between cloud and mobile devices

Mobile cloud computing has recently emerged as a new computing paradigm promising to improve the capabilities of resource-constrained mobile devices. As the data processing and storage are moved from mobile devices to powerful cloud platforms, data transmission has become an important issue affecting user experiences of mobile applications. One of the challenges is how to optimize the tradeoff between system throughput and energy consumption, which are potentially conflicting objectives. Inspired by the feasibility of transmission scheduling for prefetching-friendly or delay-tolerant applications, we mathematically formulate this problem as a stochastic optimization problem, and design an online control algorithm to balance such an energy-performance tradeoff based on the Lyapunov optimization framework. Our algorithm is able to independently and simultaneously make control decisions on admission and transmission to maximize a joint utility of the average application throughput and energy cost, without requiring any statistical information of traffic arrivals and link bandwidth. Rigorous analysis and extensive simulations have demonstrated both the system stability and the utility optimality achieved by our control algorithm.

Reliable broadcast transmission in wireless networks based on network coding

Recently, XOR based network coding has been applied to the loss recovery of reliable broadcast transmission in wireless networks, where the source can XOR multiple lost packets with distinct receivers together into one packet and transmits this combined packet in one retransmission, resulting in a significant improvement on transmission efficiency. The problem of finding the optimal XOR coding set that minimizes the overall number of transmissions for loss recovery has been proved to be NP-hard. In this paper, we propose an efficient heuristic algorithm based on vertex coloring for giving an approximately optimal solution to address the problem, and then apply this heuristic algorithm into the existing XOR-based retransmission schemes. Simulation results are given to demonstrate superior performance of our algorithm over previously proposed works.

Optimal scheduling for data transmission between mobile devices and cloud

Mobile cloud computing has emerged as a new computing paradigm promising to extend the capabilities of resource-constrained mobile devices. In this new paradigm, mobile devices are enabled to offload computing tasks, report sensing records, and store large files on the cloud through wireless networks. Therefore, efficient data transmission has become an important issue affecting user experiences on mobile cloud. Considering the limited battery energy of mobile devices and different application requirements on transmission delay, this study presents an online control algorithm (OPERA) based on the Lyapunov optimization theory for optimally scheduling data transmission between mobile devices and cloud. The OPERA algorithm is able to make control decisions on application scheduling, interface selection and packet dropping to minimize a joint utility of network energy cost and packet dropping penalty, without requiring any statistical information of traffic arrivals and link throughputs. Rigorous analysis and extensive simulations have demonstrated its distinguished performance in terms of utility optimality, system stability and service delay.

A Stochastic Control Approach to Maximize Profit on Service Provisioning for Mobile Cloudlet Platforms

The recent emergence of mobile cloud computing has enabled mobile users to offload computing tasks from mobile devices to nearby cloudlets, so as to reduce energy consumption and improve application performance. In this paper, we consider the problem of maximizing the profit of the cloudlets’ managing platform that receives computing requests from mobile users and fulfils these requests by leveraging computing service of participating cloudlets. However, it is very challenging to maximize the operating profit for such a managing platform, due to unpredictable arrival of user requests, dynamic participation of mobile cloudlets, and complexity in computing resource allocations. Based on the Lyapunov optimization technique combined with the technique of weight perturbation, we introduce a new stochastic control algorithm that makes online decisions on computing request admission and dispatching, computing service purchasing, and computing resource allocation. Different from traditional techniques, this algorithm does not require any statistical knowledge of relevant system dynamics, and is efficient for implementation in practice. Theoretical analysis and simulation results have demonstrated both the profit optimality and the system stability achieved by the proposed control algorithm.

Diamond: An Improved Fat-tree Architecture for Large- scale Data Centers

With the widespread deployment of cloud services, data center networks are developing toward large-scale, multi- path networks. To support the new trend, some novel network architectures have been proposed, and Fat-tree is one of the most promising architecture and gets a lot of attention because it has good performance on aggregate bandwidth with a simple topology. This paper presents Diamond, an improved Fat-tree architecture for large-scale data centers. Diamond replaces all the aggregation switches of Fat-tree with edge switches and connects directly edge switches to core switches. By this alteration, the average route path and End-to-End (ETE) delay in Diamond are 10% less than that in Fat-tree. We design FAR, a simple and high-efficient routing method for Diamond. In FAR, each switch requires only hundreds of route entries to support a large-scale network with tens of thousands servers, and to build FAR routing tables, switches exchange very few messages. Diamond and its FAR routing are verified through OPNET simulations. 

Energy-Efficient Network Transmission between Satellite Swarms and Earth Stations Based on Lyapunov Optimization Techniques

The recent advent of satellite swarm technologies has enabled space exploration with a massive number of picoclass, low-power, and low-weight spacecraft. However, developing swarm-based satellite systems, from conceptualization to validation, is a complex multidisciplinary activity. One of the primary challenges is how to achieve energy-efficient data transmission between the satellite swarm and terrestrial terminal stations. Employing Lyapunov optimization techniques, we present an online control algorithm to optimally dispatch traffic load among different satellite-ground links for minimizing overall energy consumption over time. Our algorithm is able to independently and simultaneously make control decisions on traffic dispatching over intersatellite-links and up-down-links so as to offer provable energy and delay guarantees, without requiring any statistical information of traffic arrivals and link condition. Rigorous analysis and extensive simulations have demonstrated the performance and robustness of the proposed new algorithm.

Optimal scheduling for energy harvesting mobile sensing devices

The rapid advances in mobile devices and their embedded sensors have enabled a compelling paradigm for collecting ubiquitous data to share with each other or the general public. In this paper, we study how to achieve the close-to-optimal transmission utility performance for sensor-enhanced mobile devices that are capable of harvesting energy from the environment. This is a very challenging task due to the stochastic and unpredictable nature of data arrival, channel condition, and energy replenishment. By taking advantage of the Lyapunov optimization framework, we propose an online scheduling algorithm called OSCAR (Optimal SCheduling AlgoRithm), which jointly make control decisions on system state, energy harvesting, and data transmission for achieving optimal utility on mobile sensing devices. Different from traditional techniques, OSCAR does not require any knowledge of system statistics, including the energy state process. Rigorous analysis and extensive experiments have demonstrated both the system stability and the utility optimality achieved by the OSCAR algorithm.